ZacharyZhang-NY
e74dafcd36
Three independently ugly bugs that all fed the same log flood:
* Electron's `WebFrameMain.send` writes every "Render frame was disposed"
to stderr via `console.error`. When the worker fans a second
agent-event past a reloading renderer, we produced thousands of
identical lines and stalled the main loop on stderr I/O. Added a
`mainFrame.detached` guard so the send is short-circuited before
Electron's own catch block fires, and installed a targeted filter in
`suppress-warnings.ts` (now actually imported from the entry point)
so the specific `console.error("Error sending from webFrameMain:"…)`
noise never reaches the terminal. Any other payload still surfaces.
* `dispatchAgentMessage` accepted the human-reader sentinel as a wake
target when an agent replied to `__board__` — `wakeAgentIfPossible`
then called `db.getAgent("__board__")`, threw, and the dispatcher
kept repeating on every send. Strip `HUMAN_SENDER_ID` at
`resolveMessageWakeTargets`, and keep a defense-in-depth guard in the
heartbeat so a stray caller (future automation fan-out) cannot
reintroduce the crash.
* Pi's skills loader rejects any SKILL.md whose frontmatter `name`
diverges from its parent directory. The four bundled `my-writing-style`
copies declared `name: writing-style-skill`, so every install emitted
a warning and dropped the skill. They also lived as orphan gitlinks
(mode 160000 without .gitmodules), and the global `*.md` gitignore
hid every other SKILL.md from version control. Fixed the frontmatter
to match the dir, converted the dangling submodules to plain trees,
and allowlisted `builtin-skills/**/*.md` so a fresh clone carries the
catalogue instead of silently losing it.
4.8 KiB
design-dna
https://github.com/user-attachments/assets/00e0a28d-42ce-4a08-a0c0-1ecf8b9f7e97
Other cases
https://github.com/user-attachments/assets/80793608-930d-42ca-951f-eb21ac188d54
https://github.com/user-attachments/assets/cd4cba94-cd2c-480f-8efa-4ac86e00ae1f
English | 中文
An agent skill for extracting, structuring, and applying visual design identity as machine-readable "Design DNA" across three dimensions: design tokens, qualitative style, and visual effects.
Prerequisites
- Node.js environment installed
- Ability to run
npxcommands
Installation
Quick Install (Recommended)
npx skills add zanwei/design-dna
Install to Specific Agent
# Cursor only, non-interactive, global install
npx skills add zanwei/design-dna -a cursor -g -y
# Claude Code only
npx skills add zanwei/design-dna -a claude-code -g -y
Install from Local Clone
git clone https://github.com/zanwei/design-dna.git
npx skills add ./design-dna -y
List Available Skills
npx skills add zanwei/design-dna --list
What It Does
| Dimension | Role |
|---|---|
| Design System | Measurable tokens: color, typography, spacing, layout, shape, elevation, motion, components |
| Design Style | Qualitative perception: mood, visual language, composition, imagery, interaction feel, brand voice |
| Visual Effects | Beyond plain CSS: Canvas, WebGL, 3D, particles, shaders, scroll-driven motion, cursor effects, SVG animation, glassmorphism, etc. |
The skill drives a three-phase workflow:
- Structure — Surface the full schema and field meanings (see
references/schema.md). - Analyze — From screenshots, images, or URLs, produce a complete JSON profile (every field filled; conflicts noted).
- Generate — Given DNA JSON plus content, implement the design (default: self-contained HTML/CSS/JS), following
references/generation-guide.md.
Phases can be used alone or chained (e.g. Analyze → Generate).
How It Works
Pipeline at a glance (Mermaid renders on GitHub):
flowchart LR
A["Reference designs<br/>Screenshots · URLs · images<br/><br/>Any design you admire"]
B["Design DNA JSON<br/>Quantified spec<br/><br/>Structured profile"]
C["Final output<br/>Faithful implementation<br/><br/>Production-ready UI"]
A -->|"Analyze — extract every visual property"| B
B -->|"Generate — apply DNA to your content"| C
B -.-> D["Save · reuse · version control"]
Step 1 — Curate references. Collect screenshots, images, or live URLs of designs whose visual identity you want to capture. Multiple references can be combined; the skill identifies dominant patterns and notes variants.
Step 2 — Extract DNA. Feed the references to the agent. It inspects every visual property across all three dimensions and outputs a complete, quantified Design DNA JSON — no empty fields, no guesswork. This JSON becomes a portable, reusable design specification.
Step 3 — Generate from DNA. Provide the DNA JSON together with your own content. The agent produces implementations that faithfully reproduce the original design language while adapting to your material.
The DNA JSON is the key artifact. Once extracted, it can be committed to version control, shared across teams, reused across projects, and iteratively refined — turning subjective "make it look like that site" into a precise, reproducible specification that any agent can consume.
Tip
Refining visual richness. If the first pass still feels visually thin or under-detailed next to your references, run a deliberate polish iteration: re-attach the same URLs or screenshots. This narrows the gap between a workable draft and a reference-faithful, visually rich result without starting over.
Prompt: Against the reference, audit hierarchy, ornamentation, typographic rhythm, motion, materiality, and overall UI—then merge your conclusions back into the current implementation.
Compatibility
Follows the Agent Skills specification. Installable via skills CLI to all supported agents including Cursor, Claude Code, Codex, GitHub Copilot, and 39 more.
Contributing
Issues and pull requests are welcome. For substantive behavior changes, update SKILL.md and any affected files under references/ so the skill stays internally consistent.
License
MIT